Voronoi Diagrams and Perlin Noise for Simulation of Irregular Artefacts in Microscope Scans

Artefacts are a common occurrence in microscopic images and scans used in life science research. The artefacts may be regular and irregular and arise from different sources: distortions of the illumination field, optical aberrations, foreign particles in the illumination and optical path, errors, irregularities during the processing and staining phases, et cetera. While several computational approaches for dealing with patterned distortions exist, there is no universal, efficient, reliable, and facile method for removing irregular artefacts. This leaves life scientists within cumbersome predicaments, wastes valuable time, and may alter the analysis results. In this article, the authors outline a systematic way to introduce synthetic irregular artefacts in microscopic scans via Perlin Noise and Voronoi Diagrams. The reasoning behind such a task is to produce pairs of “successful” and manufactured “failed” image counterparts to be used as training pairs in an artificial neural network tuned for artefact removal. At the moment, the outlined method only works for grayscale